Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

摘要

Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender's predicted ratings are to the users' true ratings. However, it has been recognized that other recommendation qualities-such as whether the list of recommendations is diverse and whether it contains novel items-may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of "beyond accuracy" objectives.